Higher-Order Visual Decision Making Networks and Mechanisms

高阶视觉决策网络和机制

• 批准号: 8957154
• 负责人: ANDREW S KAYSER
• 金额: $ 43.19万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8957154
• 关键词: Higher; Order; Visual; Decision; Making

DESCRIPTION (provided by applicant): Defining the structure of a canonical decision making circuit, from its inputs to its outputs, is one of the principal goals of cognitive neuroscience. T this end, visual perceptual decision making is a fundamental cognitive function in which visual information provides the basis for choosing an appropriate response. In the simplest case, the link between sensory evidence and a behavioral choice is binary: an oncoming car suddenly veers into one's lane and a quick decision must be made - to swerve left or right. However, when vision is compromised, whether through disease or trauma, these decision mechanisms must adapt to degraded input, impaired associative visual processing, or both. As a consequence, visual misperceptions occur more frequently, and the process of translating sensation to action becomes increasingly susceptible to errors of identification and categorization that must be recognized. Understanding the neural mechanisms by which visual perception interacts with higher-order categorization and error detection is thus essential to understanding abnormalities in patients. In this proposal, we build upon previous work studying perceptual decision making in primates and humans to evaluate the process by which humans make higher-order visual decisions. One hypothesis is that both lower-order (i.e. perceptual) and higher-order (e.g. categorization) decisions may differ in the source of their inputs but are mediated by the same decision making network. An alternative hypothesis argues that categorical uncertainty, whether related to the separation of object classes or to the separation of correct from erroneous responses, represents a more abstract feature related to confidence and context that engages different decision processes. Under this hypothesis, perceptual and categorical uncertainty should activate different circuits. By using a combination of behavioral psychophysics, mathematical models, functional MRI, and EEG in a well-validated visual paradigm, here we will attempt to define the brain networks and mechanisms that allow humans to make visual categorization decisions and to detect errors.

描述（由申请人提供）：定义规范决策电路的结构，从其输入到其输出，是认知神经科学的主要目标之一。 T视觉感知决策是一种基本的认知功能，在该功能中，视觉信息为选择适当的响应提供了基础。在最简单的情况下，感官证据与行为选择之间的联系是二进制的：迎面而来的汽车突然转向一个人的车道，必须做出快速的决定 - 左右转。但是，当视力受到损害时，无论是通过疾病还是创伤，这些决策机制都必须适应降解的输入，关联视觉处理受损或两者兼而有之。结果，视觉误解发生频率更高，将感觉转化为动作的过程越来越容易受到必须识别的识别和分类错误的影响。因此，了解视觉感知与高阶分类和错误检测相互作用的神经机制对于理解患者的异常是必不可少的。在此提案中，我们基于先前研究灵长类动物和人类的感知决策的工作，以评估人类做出高阶视觉决策的过程。一个假设是，低阶（即感知）和高阶（例如分类）决策的决策在其输入的来源可能有所不同，但由相同的决策网络介导。另一种假设认为，分类不确定性是与对象类别的分离或与错误响应的分离有关，代表了与置信度和背景有关的更抽象的特征，与不同的决策过程有关。在这一假设下，感知和分类不确定性应激活不同的电路。通过在经过良好验证的视觉范式中使用行为心理物理学，数学模型，功能性MRI和EEG的组合，我们将尝试定义大脑网络和机制，使人类可以做出视觉分类决策并检测错误。